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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title:	Modelling of the Strength of High Performance Concrete using Machine Learning Models
Authors Name:	Sangram Biplab Manabendra Thakur , Paresh Biswal
Unique Id:	IJSDR2105032
Published In:	Volume 6 Issue 5, May-2021
Abstract:	Many research work has shown that High-performance concrete may be a profoundly complex fabric, which makes modelling its behaviour a very troublesome assignment. This paper is pointed at illustrating the conceivable outcomes of adapting the leading conceivable machine learning model to anticipate the compressive strength of high-performance concrete. We are using a data set where the input components are as follows Cement -- kg per m3, Blast Furnace Slag -- kg per m3, Fly Ash -- kg per m3, Water -- kg per m3, SP's -- kg per m3, Coarse Aggregate -- kg per m3, Fine Aggregate -- kg per m3, Age -- Day (1~365). Whereas my output components will be Concrete compressive strength -- MPa. The present study leads to the following conclusion Bootstrap Random Forest classification model performance is better than other machine learning algorithms. Subsequently utilizing the machine learning model will not as it were offer assistance in foreseeing the strength but moreover will be valuable in making a prediction of materials required eventually it'll offer assistance in lessening the wastage of material
Keywords:	Fly ash (FA), Ground Granulated Blast Furnace Slag (GGBS), compressive strength, machine learning models
Cite Article:	"Modelling of the Strength of High Performance Concrete using Machine Learning Models", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.6, Issue 5, page no.174 - 179, May-2021, Available :http://www.ijsdr.org/papers/IJSDR2105032.pdf
Downloads:	000337072
Publication Details:	Published Paper ID: IJSDR2105032 Registration ID:193304 Published In: Volume 6 Issue 5, May-2021 DOI (Digital Object Identifier): Page No: 174 - 179 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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